AI Software Technical Intern

AI in this role

anthropiccopilotpytorch

prompt-engineering

Job Details:

Job Description:

Role Overview

We are seeking a driven and curious Technical Intern to join our AI Software and Hardware Architecture team. In this role, you will work at the intersection of next-generation GPU architecture, performance simulation, and modern AI engineering.

Our team develops architectural models and virtual platforms (VPs) to project, evaluate, and optimize the performance of future high-performance computing (HPC) and AI GPU architectures before silicon is taped out. As part of this mission, you will actively integrate the latest generative AI capabilities-including GitHub Copilot, custom Large Language Model (LLM) agents, and developer tooling integrations-directly into our architectural modeling, automated testing, and performance analysis pipelines.

This internship offers an opportunity to gain hands-on experience with production-scale architectural simulators while pioneering modern, AI-augmented hardware engineering practices.

Key Responsibilities

Virtual Platform Development and Architecture Simulation

Collaborate with senior architects to develop, calibrate, and validate cycle-approximate or cycle-accurate Virtual Platform (VP) models for future GPU architectures (compute engines, memory fabric, cache hierarchies, and interconnects).
Implement functional and performance simulation components in modern C++ and SystemC/TLM frameworks.
Assist in porting, configuring, and executing graphics and compute workloads (e.g., PyTorch, SYCL, OpenCL, Vulkan/DirectX) across simulated environments.

AI-Augmented Workflow Integration and Automation

Accelerate development and debugging efficiency by leveraging GitHub Copilot and state-of-the-art LLM capabilities across team repositories.
Prototype and deploy agentic AI workflows and tools (e.g., Model Context Protocol [MCP] integrations, automated log and trace summarization, error diagnosis).
Build AI-assisted test generation tools and validation scripts using Python and unit testing frameworks (e.g., GoogleTest).

Performance Analysis and Telemetry

Collect, profile, and analyze trace-driven simulation data to pinpoint memory subsystem bottlenecks, cache miss penalties, and pipeline stalls.
Create automated visualization dashboards and reporting scripts to present performance trade-offs to senior architecture and design teams.
Investigate anomaly detection in large-scale simulation telemetry using machine learning and statistical analysis techniques.

Qualifications:

Minimum Qualifications

Currently enrolled in an accredited Master's or Ph.D. program in Computer Science, Computer Engineering, Electrical Engineering, or a closely related discipline.
Strong proficiency in C++ (modern C++17/20 preferred) and Python.
Solid foundational knowledge of computer architecture principles (cache hierarchies, memory coherence, pipelining, and vector/SIMD processing).
Experience with Linux/Unix environments, version control systems (Git), and modern build pipelines (CMake, Ninja).
Familiarity with utilizing AI developer tooling (e.g., GitHub Copilot, Anthropic APIs, or local LLM runtimes) for software engineering.

Preferred Qualifications

Prior coursework or project experience in:

GPU architecture, parallel programming, or accelerated computing (CUDA, SYCL, OpenCL, or GPU compute APIs).
Virtual Platforms, architectural simulators (e.g., gem5, SystemC/TLM, or proprietary simulator platforms).
Software testing and test-driven development (TDD) using frameworks like GoogleTest (gtest).
Experience building LLM-assisted developer agents, prompt engineering pipelines, or custom developer automation tooling.
Familiarity with hardware/software co-design concepts and performance modeling methodologies.

What we offer

Mentorship from industry-leading Principal Engineers and Architects in GPU architecture and AI system design.
Exposure to pre-silicon hardware validation methodologies used across world-class compute products.
An innovative environment where you are encouraged to experiment with and deploy the latest generative AI paradigms into traditional systems engineering.
Competitive intern compensation, technical networking events, and career development sessions

We invite you to join us in shaping the future of AI technology and making a meaningful impact on the world. This role offers an exciting opportunity to grow, innovate, and contribute to Intel's mission of delivering world-class solutions for a connected and intelligent future.

Please note, this is a 6-month internship opportunity located in Santa Clara, CA. Target start date is January - 2027.

Job Type:

Student / Intern

Shift:

Shift 1 (United States of America)

Primary Location:

US, California, Santa Clara

Additional Locations:

Posting Statement:

All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.

Position of Trust

N/A

Benefits

We offer a total compensation package that ranks among the best in the industry. It consists of competitive pay, stock bonuses, and benefit programs which include health, retirement, and vacation. Find out more about the benefits of working at Intel.

Annual Salary Range for jobs which could be performed in the US: $133,498.00-170,602.00 USD

The range displayed on this job posting reflects the minimum and maximum target compensation for the position across all US locations. Our standard internship rates are based on your degree, location, and the job role. Your recruiter can share more about the specific compensation range for your preferred location and job role during the hiring process.

Work Model for this Role

This role will be eligible for our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. * Job posting details (such as work model, location or time type) are subject to change.

ADDITIONAL INFORMATION: Intel is committed to Responsible Business Alliance (RBA) compliance and ethical hiring practices. We do not charge any fees during our hiring process. Candidates should never be required to pay recruitment fees, medical examination fees, or any other charges as a condition of employment. If you are asked to pay any fees during our hiring process, please report this immediately to your recruiter.

How we rate this

AI Software Technical Intern at Intel rates 88 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Prompt EngineeringAnthropicCopilotPyTorch

Questions you could be asked

How do you structure and test a prompt to get consistent output from a language model?
Walk me through how you've used Anthropic in your day-to-day work.
What are the limits of Copilot that you've run into, and how did you work around them?
What's a project where you used PyTorch hands-on?
How would you decide a model or AI system is ready to ship?

Adapt your resume

List these exact terms on your resume: Prompt Engineering, Anthropic, Copilot, and PyTorch. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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